


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
						from PIL import Image







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Optional[Any] 			=		'dandelin/vilt-b32-finetuned-vqa'
			_UpperCamelCase		:       Any 			=		(
			    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
			    'image containing the information, as well as a `question` which should be the question in English. It '
			    'returns a text that is the answer to the question.'
			)
			_UpperCamelCase		:       Optional[int] 			=		'image_qa'
			_UpperCamelCase		:       int 			=		AutoProcessor
			_UpperCamelCase		:       Dict 			=		AutoModelForVisualQuestionAnswering

			_UpperCamelCase		:       Tuple 			=		['image', 'text']
			_UpperCamelCase		:       int 			=		['text']




			def __init__(				self   ,    *snake_case   ,    **snake_case  ):

						'''simple docstring'''


						requires_backends(self   ,    ["vision"]  )
						super().__init__(*snake_case   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						return self.pre_processor(snake_case   ,    snake_case   ,    return_tensors="pt"  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						with torch.no_grad():
									return self.model(**snake_case  ).logits







			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							outputs.argmax(-1  ).item()
						return self.model.config.idalabel[idx]






import requests
from bsa import BeautifulSoup
def 							UpperCamelCase_(					_A :str = "AAPL"	)->		str:
			UpperCamelCase__       =							F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
			UpperCamelCase__       =							BeautifulSoup(requests.get(_A	).text					,  "html.parser"	)
			UpperCamelCase__       =							"My(6px) Pos(r) smartphone_Mt(6px)"
			return soup.find("div"					,  class_=class_	).find("span"	).text


if __name__ == "__main__":
						for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
												print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')


